1. Field of the Invention
The system and method of the present invention relates to the translation of correlated data from one format to another. More particularly, the system and method of the present invention relates to the application of classified adaptive filtering technology to the creation of image data at temporal and spatial coordinates that differ from those of the input data.
2. Art Background
For many applications, it is necessary to convert from one digital image format to another. These applications vary widely in conceptual difficulty and quality. Among the easiest conversions are those which do not require a change in data point location. For example, RBG to YUV format conversion, and GIF to JPEG format conversion do not require a change in data point location. Conversions which alter or reduce the number of data points are more difficult. This type of conversion occurs, for example, when an image is reduced in size. But, the most difficult type of image conversion is that which requires additional data points to be generated at new instances of time. Examples of these include converting film to video, video in PAL format to NTSC format, and temporally compressed data to a full frame-rate video.
Conventional techniques for creating data points at new instances in time include sample and hold, temporal averaging, and object tracking. The sample and hold method is a method in which output data points are taken from the most recently past moment in time. This method is prone to causing jerky motion since the proper temporal distance is not maintained between sample points.
Temporal averaging uses samples weighted by temporal distance. The primary advantage to this technique is that there is no unnatural jerkiness. One disadvantage is that there is a significant loss of temporal resolution that becomes especially apparent at the edges of fast moving objects.
Object tracking associates motion vectors with moving objects in the image. The motion vector is then used to estimate the object's position between image frames. There are two main drawbacks: it is computationally expensive, and the estimation errors may be quite noticeable.